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Algorithm 1 Diameter-regularized Hessian weighted K-means in vector case |
Input: Weights vector , Hessian matrices , diameter regularizer , number of clusters K, iterations T
Initialize the K cluster centers randomly
for to T do
Assignment step:
Initialize for all .
for to do
Assign to the nearest cluster center, i.e., find and let
end for
Update step:
Find current farthest pair of centers .
Update and by
for to K, do
Update the cluster centers by
end for
end for
Output: centers and assignments .
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